---
title: Chunkers Overview
sidebarTitle: Overview
description: Overview of the different chunkers available in Chonkie
icon: 'list'
iconType: 'solid'
---

Chonkie provides multiple chunking strategies to handle different text processing needs. Each chunker in Chonkie is designed to follow the same core principles outlined in the [concepts](/getting-started/concepts) page.

<CardGroup cols={2}>
  <Card
    title="TokenChunker"
    icon="scissors"
    href="/python-sdk/chunkers/token-chunker"
  >
    Splits text into fixed-size token chunks. Best for maintaining consistent chunk sizes and working with token-based models.
  </Card>
  <Card
    title="SentenceChunker"
    icon="align-left"
    href="/python-sdk/chunkers/sentence-chunker"
  >
    Splits text at sentence boundaries. Perfect for maintaining semantic completeness at the sentence level.
  </Card>
  <Card
    title="RecursiveChunker"
    icon="chart-tree-map"
    href="/python-sdk/chunkers/recursive-chunker"
  >
    Recursively chunks documents into smaller chunks. Best for long documents with well-defined structure.
  </Card>
  <Card
    title="SemanticChunker"
    icon="magnet"
    href="/python-sdk/chunkers/semantic-chunker"
  >
    Groups content based on semantic similarity. Best for preserving context and topical coherence.
  </Card>
  <Card
    title="SDPMChunker"
    icon="layer-group"
    href="/python-sdk/chunkers/sdpm-chunker"
  >
    Chunks using Semantic Double-Pass Merging (SDPM) algorithm, best for maintaining topical coherence when text has frequent breaks.
  </Card>
  <Card
    title="LateChunker"
    icon="clock"
    href="/python-sdk/chunkers/late-chunker"
  >
    Chunks using Late Chunking algorithm, best for higher recall in your RAG applications.
  </Card>
  <Card
    title="CodeChunker"
    icon="laptop"
    href="/python-sdk/chunkers/code-chunker"
  >
    Splits code based on its structure using ASTs. Ideal for chunking source code files.
  </Card>
  <Card
    title="NeuralChunker"
    icon="brain"
    href="/python-sdk/chunkers/neural-chunker"
  >
    Uses a fine-tuned BERT model to split text based on semantic shifts. Great for topic-coherent chunks.
  </Card>
  <Card
    title="SlumberChunker"
    icon="wand-magic-sparkles"
    href="/python-sdk/chunkers/slumber-chunker"
  >
    Agentic chunking using generative models (LLMs) via the Genie interface for S-tier chunk quality. 🦛🧞
  </Card>
</CardGroup>

## Availability

Different chunkers are available depending on your installation:

| Chunker         | Default | embeddings | 'all' | Chonkie Cloud |
|-----------------|:-------:|:----------:|:-----:|:-------------:|
| TokenChunker    | ✅      | ✅         | ✅    | ✅            |
| SentenceChunker | ✅      | ✅         | ✅    | ✅            |
| RecursiveChunker| ✅      | ✅         | ✅    | ✅            |
| CodeChunker     | ❌      | ✅         | ✅    | ✅            |
| SemanticChunker | ❌      | ✅         | ✅    | ✅            |
| SDPMChunker     | ❌      | ✅         | ✅    | ✅            |
| LateChunker     | ❌      | ✅         | ✅    | ✅            |
| NeuralChunker   | ❌      | ✅         | ✅    | ✅            |
| SlumberChunker  | ❌      | ✅         | ✅    | ✅            |

## Common Interface

All chunkers share a consistent interface:

```python
# Single text chunking
chunks = chunker.chunk(text)

# Batch processing
chunks = chunker.chunk_batch(texts)

# Direct calling
chunks = chunker(text)  # or chunker([text1, text2])
```

## F.A.Q.

<AccordionGroup>
  <Accordion title="Are all the chunkers thread-safe?" icon="reel">
    Yes, all the chunkers are thread-safe. Though, the performance might vary since some chunkers use threading under the hood. So, monitor your performance accordingly.
  </Accordion>
</AccordionGroup>